Abstract
Integrating weather forecasts into decision support systems empowers farmers to optimise irrigation schedules, thereby boosting crop yields and conserving water. However, inaccurate forecasts can jeopardise productivity and irrigation efficiency. This study combines a crop model with a stochastic pseudo-weather forecast algorithm to: (1) determine the reliability needed in a weather forecast algorithm for effective irrigation management; and (2) assess the impact of weather forecast reliability on the productivity and environmental footprint of various maize cropping systems across diverse climates. It employs the Next Generation of Agricultural Production Systems sIMulator (APSIM NextGen) to simulate maize growth at eleven locations representing diverse climates globally. Various planting schedules, soil types, irrigation systems, and nitrogen availability levels were considered to examine the effects of perfect and imperfect weather forecasts. The findings underscore the potential of integrating weather forecasts into irrigation management for enhanced productivity and sustainability. High-confidence forecasts and longer lead times increase yields (up to 11 %) and improve sustainability outcomes, particularly in wetter climates and for conditions with low nitrogen availability. Conversely, when the accuracy of forecasts is low, forecast-driven irrigation management may lead to yield reductions compared to a baseline system, especially in drier climates (up to 26 % reduction), necessitating tailored management strategies. Soil type and farmer's risk tolerance further influence the effectiveness of forecast-driven irrigation management, emphasising the need for context-specific approaches. By understanding and leveraging the interconnected impacts of weather forecasts on yield, water use efficiency, nitrogen loss, and greenhouse gas emissions, farmers can optimise productivity while minimising environmental impacts.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 5
Claim · model-written summary
High confidence forecasts and longer lead times increased simulated maize yields by up to 11%, especially in wetter climates and under low nitrogen.
- Treatment
- High-confidence forecasts and longer lead timese0
- Outcome
- yieldse0
- Direction
- increasee0
- Value
- up to 11 %e0
- Qualifier
- particularly in wetter climates and for conditions with low nitrogen availabilitye0
Zea maysNot reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“High-confidence forecasts and longer lead times increase yields (up to 11 %) and improve sustainability outcomes, particularly in wetter climates and for conditions with low nitrogen availability.”
t0“maize”
Claim · model-written summary
High confidence forecasts and longer lead times improved simulated sustainability outcomes.
- Treatment
- High-confidence forecasts and longer lead timese0
- Outcome
- sustainability outcomese0
- Direction
- improvee0
- Qualifier
- particularly in wetter climates and for conditions with low nitrogen availabilitye0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“High-confidence forecasts and longer lead times increase yields (up to 11 %) and improve sustainability outcomes, particularly in wetter climates and for conditions with low nitrogen availability.”
t0“maize”
Claim · model-written summary
Low forecast accuracy could reduce simulated maize yield by up to 26% relative to a baseline, particularly in drier climates.
- Treatment
- forecast-driven irrigation managemente0
- Outcome
- yielde0
- Direction
- reductionse0
- Value
- up to 26 % reductione0
- Qualifier
- especially in drier climatese0
- Comparator
- a baseline systeme0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Conversely, when the accuracy of forecasts is low, forecast-driven irrigation management may lead to yield reductions compared to a baseline system, especially in drier climates (up to 26 % reduction), necessitating tailored management strategies.”
t0“maize”
Claim · model-written summary
Soil type influenced the effectiveness of forecast driven irrigation management.
Not reportedSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Soil type and farmer's risk tolerance further influence the effectiveness of forecast-driven irrigation management, emphasising the need for context-specific approaches.”
t0“maize”
Claim · model-written summary
Farmer risk tolerance influenced the effectiveness of forecast driven irrigation management.
Not reportedSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Soil type and farmer's risk tolerance further influence the effectiveness of forecast-driven irrigation management, emphasising the need for context-specific approaches.”
t0“maize”
The same questions in other papers
lead atom · yield · Zea mays (maize)Positive and negative results
1 paper in the same direction as this paper · 2 in the opposite direction
- negative
- no effect
- positive
irrigation · yield · Zea mays (maize)Positive and negative results
224 papers in the same direction as this paper · 791 in the opposite direction
- positive
- positive
- positive
1,012 papers more in this group
See all 1,016 papers →Claims are grouped by organism, treatment and what was measured or targeted. A paper counts as positive when any of its claims in the group is positive.